Arguments

formula

symbolic description of the model, see details.

weights

optional numeric vector of weights.

data, subset, na.action

arguments controlling formula processing
via model.frame.

offset

optional numeric vector with an a priori known component to be
included in the linear predictor of the count model. See below for more
information on offsets.

standardize

Logical flag for x variable standardization, prior to
fitting the model sequence. The coefficients are always returned on
the original scale. Default is standardize=TRUE.

family

character specification of count model family (a log link is
always used).

link

character specification of link function in the binary
zero-inflation model (a binomial family is always used).

model, y, x

logicals. If TRUE the corresponding components
of the fit (model frame, response, model matrix) are returned.

penalty

penalty considered as one of enet, mnet, snet.

start

starting values for the parameters in the linear predictor.

nlambda

number of lambda value, default value is 100. The sequence may be truncated before nlambda is reached if a close to saturated model for the zero component is fitted.

lambda.count

A user supplied lambda.count sequence. Typical usage
is to have the
program compute its own lambda.count and lambda.zero sequence based on
nlambda and lambda.min.ratio.

lambda.zero

A user supplied lambda.zero sequence.

penalty.factor.count, penalty.factor.zero

These are numeric vectors with the same length as predictor variables. that multiply lambda.count, lambda.zero, respectively, to allow differential shrinkage of coefficients. Can be 0 for some variables, which implies
no shrinkage, and that variable is always included in the
model. Default is same shrinkage for all variables.

lambda.count.min.ratio, lambda.zero.min.ratio

Smallest value for lambda.count
and lambda.zero, respectively, as a fraction of
lambda.max, the (data derived) entry value (i.e. the smallest
value for which all coefficients are zero except the intercepts).
Note, there is a closed formula for lambda.max for penalty="enet". If rescale=TRUE, lambda.max is the same for penalty="mnet" or "snet". Otherwise, some modifications are required. In the current implementation, for small gamma value, the square root of the computed lambda.zero[1] is used when penalty="mnet" or "snet".

alpha.count

The elastic net mixing parameter for the count part of model.

alpha.zero

The elastic net mixing parameter for the zero part of model.

gamma.count

The tuning parameter of the snet or mnet penalty for the count part of model.

gamma.zero

The tuning parameter of the snet or mnet penalty for the zero part of model.

rescale

logical value, if TRUE, adaptive rescaling

init.theta

The initial value of theta for family="negbin".

theta.fixed

Logical value only used for family="negbin". If TRUE, theta is not updated.

EM

Using EM algorithm. Not implemented otherwise

convtype

convergency type, default is for count component only for speedy computation

maxit.em

Maximum number of EM algorithm

maxit

Maximum number of coordinate descent algorithm

maxit.theta

Maximum number of iterations for estimating theta scaling parameter if family="negbin". Default value maxit.theta may be increased, yet may slow the algorithm

eps.bino

a lower bound of probabilities to be claimed as zero, for computing weights and related values when family="binomial".

reltol

Convergence criteria, default value 1e-5 may be reduced to make more accurate yet slow

shortlist

logical value, if TRUE, limited results return

trace

If TRUE, progress of algorithm is reported

...

Other arguments which can be passed to from glmreg

Details

The algorithm fits penalized zero-inflated count data regression models using the coordinate descent algorithm within the EM algorithm.
The returned fitted model object is of class "zipath" and is similar
to fitted "glm" and "zeroinfl" objects. For elements such as "coefficients" a list is returned with elements for the zero and count component,
respectively. For details see below.

A set of standard extractor functions for fitted model objects is available for
objects of class "zipath", including methods to the generic functions
print, coef,
logLik, residuals,
predict. See predict.zipath for more details
on all methods.

One possible reason is that the fitted model is too complex for the data. There are two suggestions to overcome the error. One is to reduce the number of variables. Second, find out what lambda values caused the problem and omit them. Try with other lambda values instead.

Value

An object of class "zipath", i.e., a list with components including

coefficients

a list with elements "count" and "zero"
containing the coefficients from the respective models,

residuals

a vector of raw residuals (observed - fitted),

fitted.values

a vector of fitted means,

weights

the case weights used,

terms

a list with elements "count", "zero" and
"full" containing the terms objects for the respective models,

theta

estimate of the additional theta parameter of the
negative binomial model (if a negative binomial regression is used),